International Journal of Engineering and Management Research
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Implementation of MQTT Protocol for Artificial Intelligence
Get PDFThe IoT-Based Attendance Management System is a sophisticated solution leveraging Azure services and MQTT protocol for efficient attendance tracking. The system comprises a Face Device equipped with facial recognition capabilities, which captures attendance data and communicates with Azure IoT Hub. The Azure IoT Hub, acting as the central hub, receives and processes attendance data from the face device, utilizing MQTT for real-time communication. A dedicated MQTT service, facilitated by the Mosquitto broker, subscribes to the Azure IoT Hub, ensuring seamless data flow between the face device and the broader system. The web application, hosted on Azure App Service and integrated with SQL Server, serves as the user interface. It not only interacts with the face device through Azure IoT Hub but also processes attendance data and generates comprehensive reports, enhancing the overall efficiency of attendance management through the power of IoT.
This architecture offers a scalable, secure, and real-time attendance management solution, bridging the physical and digital realms through facial recognition technology and Azure IoT services. The synergy of components, from the MQTT-enabled face device to the Azure-based web application, establishes a robust ecosystem for capturing, processing, and presenting attendance data with seamless integration and reliability
Medical Imaging Using Deep Learning
No full textThe healthcare sector has been transformed by deep learning, a kind of artificial intelligence Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two examples of deep learning techniques that have been used to evaluate medical pictures, forecast illness outcomes, and enhance patient care. This study examines the important strides made by deep learning in the fields of radiology, pathology, genomics, and electronic health records (EHRs). Additionally, it draws attention to the difficulties and moral issues that come with the application of deep learning in healthcare, highlighting the necessity of strong data protection and model interpretability. Deep learning\u27s potential and promising results in healthcare highlight its revolutionary effects on patient care, diagnosis, and treatment, ultimately raising the standard of care
A Study of the Effect of Foreign Exchange Rates on the Financial Performance of Power Utility Companies in Zambia: A Case of Copperbelt Energy Corporation Plc
No full textJust like other sectors, energy sector in Zambia has faced exchange rates volatility for a long time. Research has shown that Zambian kwacha has been unstable and that it has been depreciating. This study aimed at assessing the effect of foreign exchange rates on the financial performance of power utility companies in Zambia as a result of the devaluation of kwacha. It was an empirical study as the researcher sought to gain knowledge by using quantitative data. Purposive sampling was used in selecting Copperbelt Energy Corporation Plc and secondary data used in the study was extracted from the company’s published audited financial statements. Regression analysis using GraphPad software and Microsoft tools were used to analyse data and findings were presented in tables and graphs.
The main results of the study showed that foreign exchange rates had an effect on the financial performance of CEC Plc. Whenever kwacha depreciated, financial performance of the company went down and vice versa. The results further, suggests that there was a medium positive relationship between foreign exchange rates and key financial performance indicators. Henceforward, it was recommended that CEC Plc should ensure that foreign exchange risk management techniques such as money market hedge, exposure netting and hedging with invoice currency are used to minimize foreign exchange risks. It was also recommended that further studies be done in this sector using other financial performance indicators which were not employed in this study to increase the knowledge base
Sentiment Analysis of Twitter Data Using Machine Learning Techniques
No full textIn the age of social media, it is more convenient for individuals to articulate their thoughts and emotions. Each day, they disseminate their perspectives and notions on various social media platforms about ongoing global events. On controversial issues, one can find a consensus of public feeling, whether positive or negative. Twitter functions as a demonstration of a social media platform where individuals participate in discussions about their perspectives. Twitter sentiment analysis examines the overall feeling or emotion expressed in tweets. It employs machine learning and natural language processing techniques to automatically categorize tweets as good, negative, or neutral depending on their content. It may be used for single tweets or a bigger dataset relating to a specific topic or event. Through the identification of these sentiments, machine learning endows us with an advantageous position in the analysis and prediction of said sentiments. Distinct machine learning models are utilized in this paper to scrutinize sentiments within Twitter data. The proposed system offers a comprehensive evaluation of the performance of various machine learning algorithms, including Vader, XGBoost with CountVectorizer, XGBoost with Gensim, Random Forest with CountVectorizer, Random Forest with Gensim, Single LSTM, and Bidirectional LTSM and Bidirectional LTSM gives highest accuracy of .73
A Study of the Effect of Monetary Policy and International Reserves on Zambia’s Economic Growth
No full textThe objective of this study was to determine the effect of monetary policy and international reserves on economic growth in Zambia using annual time series data from 1980-2022. A number of studies have been carried in Zambia on the effect of monetary policy on Zambia’s economic growth. Therefore, the study sought to determine the effect of monetary policy and international reserves in Zambia. The variables included in the study were GDP growth (GDPTH) as the dependent variable and International reserves (FXR), exchange rate (EXR), interest rate (IR) and inflation (INF) as independent variables. Data on these variables was obtained from World Bank. The study employed a research design by using the method of ARDL analysis, the data was entered in the software package stata which was used to perform the ARDL model to estimate the long and short run relationship between the dependent variable and independent variables. The ARDL bound test was used to analysis cointegration and long run relationship and the ECM was used for analyzing short run relationship. The results showed that cointegration was there and there was a long- run relationship, all the variables were discovered to have a statistically significant in the long-run. Exchange rate and inflation have a statistically significant negative impact on economic growth in Zambia in the long- run while exchange rate is insignificant in the short-run. Interest rate and international reserves have a statistically significate positive impact on economic growth in Zambia while interest rate is insignificant after in second lag in the short run. Diagnostic test for serial correlation (Breush-Godfrey serial correlation LM test), normality (Jarque-Bera), heteroscedasticity (Breush-Pagan-Godfrey test) and stability (CUSUM-of-square test) were conducted to evaluate the estimated model. The study found that the estimated ARDL model could provide information on the behaviour monetary policy and international reserves’ impact on Zambia’s economic growth. The study recommended diversification of international reserves, exchange rate management, interest rate policies and inflation targeting
IoT Based Real Time Applications: Smart Irrigation in Agriculture
No full textBy automating farm tasks, the agricultural sector can go from being manual and static to intelligent and dynamic, increasing productivity while requiring less human oversight. This study suggests an automated irrigation system that uses automatic watering to monitor and maintain the appropriate soil moisture content. The control unit is implemented using the Arduino Uno platform and microcontroller. The system makes use of soil moisture sensors to determine the precise moisture content of the soil. This number helps the system to use the right amount of water, preventing over- or under-irrigation. Farmers are informed of sprinkler status with the usage of IoT. The system makes use of soil moisture sensors to determine the precise the content of the soil. This number helps the system to use the right amount of water, preventing over under-irrigation. Farmers are informed of sprinkler status with the usage of IOT. This method saves labor as well as water. Using this strategy, water delivery will be based on crop the soil moisture sensor is designed to identify moisture for this independent system, and the ESP8266 Wi-Fi will receive the result
Arduino Based Smart Solar Mower
Get PDFThis study recommends an Arduino-based smart solar grass cutter to increase user productivity and accessibility. This paper presents the Smart Solar Grass Cutter’s algorithm, scope, aims, applications and benefits. Grass Cutter industry heavily relies on manually operated grass cutters. However, in addition to using a significant amount of energy and contributing to air pollution, manually operated grass cutters also produce a significant amount of noise and vibration. Arduino based smart solar grass cutter is an intelligent automated grass cutter with ultrasonic sensors for obstacle detection
A Study on Chatbots and Virtual Assistants in Customer Engagement: A Review
No full textThe use of chatbots and virtual assistants in customer engagement has gained significant attention in recent years. This paper presents a comprehensive review of the literature on this topic, aiming to provide insights into the impact of chatbots and virtual assistants on customer experience, satisfaction, and loyalty. The review encompasses various aspects, including the growth and prosperity of chatbots, their impact on customer service, and their integration into wireless services. Additionally, the study explores the role of chatbots in enhancing customer engagement in digital marketing and their influence on customer loyalty. The findings from this review contribute to a better understanding of the implications of chatbots and virtual assistants in customer engagement, providing valuable insights for businesses and researchers by understanding chatbots and virtual assistants in customer engagement
Role of Retail Channel Management Strategy – In Context of Indian Automobile Dealerships Satisfaction
No full textMaintaining high-quality standards is crucial for dealerships to ensure ongoing business success since they play a critical role in promoting manufacturers in the marketplace. The study\u27s objective is to highlight areas where Original Equipment Manufacturer (OEM)-Dealers relationships are lacking in relation to crucial business metrics in order to provide practical recommendations for improving OEM-Dealer collaboration. The study utilizes primary data, collected from 141 automobile dealers in Pune using a random sampling technique and structured interviews. Secondary data is gathered in order to reinforce the study\u27s objectives. The study\u27s aims are thoroughly justified through the use of SPSS for primary data analysis (t-tests, ANOVA, regression, and correlation) and Excel for secondary data analysis. Statistical research shows a substantial association between OEM market shares and DSI (dealer satisfaction index), with a Pearson\u27s correlation coefficient = 0.850, signifying a highly significant relationship. Dealers linked to enterprises with larger market shares are more likely to show higher satisfaction ratings. The study emphasizes the significance of Original Equipment Manufacturer (OEM) participation in policy-making, revealing a strong link (Pearson correlation coefficient = 0.821) between dealer satisfaction and OEM’s involvement with policy decisions. The findings emphasize the essential standing of collaboration between OEMs and dealers and provide practical suggestions for improving business partnerships, leading to increased dealer satisfaction and overall success in the automotive sector
Optical Character Recognition from Images
No full textAnalysis of document images for information extraction has become very prominent in recent past. Wide variety of information, which has been conventionally stored on paper, is now being converted into electronic form for better storage and intelligent processing. This needs processing of documents using image analysis, processing methods. This article provides an overview of various methods used for digital image processing using three main components: Pre-processing, Feature extraction and the Classification. Pre-processing feature extraction and classification. Classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications